Paper Info
Title
Feature Hourglass Network for Skeleton Detection
Abstract
Geometric shape understanding provides an intuitive representation of object shapes. Skeleton is typical geometrical information. Lots of traditional approaches are developed for skeleton extraction and pruning, while it is still a new area to investigate deep learning for geometric shape understanding. In this paper; we build a fully convolutional network named Feature Hourglass Network (FHN) for skeleton detection. FHN uses rich features of a fully convolutional network by hierarchically integrating side-outputs with a deep-to-shallow manner to decrease the residual between the prediction result and the ground-truth. Experiment data shows that FHN achieves better performance compared with baseline on both Pixel SkelNetOn and Point SkelNetOn datasets.
Year
DOI
Venue
2019
10.1109/CVPRW.2019.00154
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Field
DocType
ISSN
Computer vision,Hourglass,Pattern recognition,Computer science,Artificial intelligence,Skeleton (computer programming)
Conference
2160-7508
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Nan Jiang100.68
Yifei Zhang213.06
Dezhao Luo351.77
Chang Liu492.13
Yu Zhou59822.73
Zhenjun Han617616.40